Michael E. Coulter

Naturalistic behavior and self-generated neural activity predictive of self-correction.

The paper develops a task for rats to study self-correction and finds distinct hippocampal representations during this process.

RealtimeDecoder: A fast software module for online clusterless decoding

RealtimeDecoder is a Python-based system for real-time clusterless decoding of neural data with low latency and no spike sorting, demonstrated in rodent hippocampus recordings.

Closed-loop modulation of remote hippocampal representations with neurofeedback

This study shows rats can learn to generate specific remote hippocampal representations through neurofeedback without external cues or behavioral reports.

Spyglass: a framework for reproducible and shareable neuroscience research

Spyglass provides a reproducible and shareable framework for neuroscience research, enabling data reuse and analysis across labs using standardized formats and pipelines.

DeepMosaic: Control-independent mosaic single nucleotide variant detection using deep convolutional neural networks

DeepMosaic detects mosaic single nucleotide variants without control samples using deep convolutional neural networks, achieving high accuracy and uncovering many variants missed by existing methods.

Somatic mosaicism in the mature brain reveals clonal cellular distributions during cortical development

The paper evaluates brain somatic mosaicism in the adult human to identify rules governing cellular distribution during embryogenesis.

Hippocampal replay of experience at real-world speeds

This study develops a state space model to analyze hippocampal replay events in rats, finding that most replay events contain spatially coherent content and often occur at real-world speeds.

Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice

This study investigates hippocampal replay during memory-guided behavior and finds that replay content reflects past experiences rather than future choices.

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia

The paper introduces a machine learning method to detect somatic L1 insertions in human brain tissue, revealing their widespread distribution in neurons and glia and potential role in neuropsychiatric diseases.

Linked-read analysis identifies mutations in single-cell DNA sequencing data

The paper introduces LiRA, a method to distinguish true mutations from artifacts in single-cell DNA sequencing data by phasing somatic variants with germline variants.

Aging and neurodegeneration are associated with increased mutations in single human neurons

The paper identifies age-related accumulation of somatic mutations in human neurons and links them to neurodegenerative diseases.

PaSD-qc: Quality control for single cell whole-genome sequencing data using power spectral density estimation

PaSD-qc evaluates scWGS library quality using power spectral density to assess amplification uniformity and identify aberrant chromosomes.